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Matplotlib Articles
Page 39 of 91
What names can be used in plt.cm.get_cmap?
Matplotlib provides numerous built-in colormaps through plt.cm.get_cmap(), and additional colormaps can be registered using matplotlib.cm.register_cmap. You can retrieve a list of all available colormap names to use with get_cmap(). Getting All Available Colormap Names Use plt.colormaps() to retrieve all registered colormap names ? from matplotlib import pyplot as plt cmaps = plt.colormaps() print("Available colormap names:") print(f"Total colormaps: {len(cmaps)}") # Show first 10 colormaps for i, name in enumerate(cmaps[:10]): print(f"{i+1}. {name}") print("...") print(f"And {len(cmaps)-10} more colormaps") Available colormap names: Total colormaps: 170 1. Accent 2. Accent_r ...
Read MorePlot multiple time-series DataFrames into a single plot using Pandas (Matplotlib)
To plot multiple time-series DataFrames into a single plot using Pandas and Matplotlib, you can overlay different series on the same axes or use secondary y-axes for different scales. Steps to Create Multiple Time-Series Plot Set the figure size and adjust the padding between subplots Create a Pandas DataFrame with time series data Set the datetime column as the index Plot multiple series using plot() method Use secondary_y=True for different scales Display the figure using show() method Example Here's how to plot multiple time-series data with different currencies ? import numpy as ...
Read MoreHow to make several legend keys to the same entry in Matplotlib?
In Matplotlib, you can create a legend entry with multiple keys (symbols) representing different lines or data series. This is useful when you want to group related plots under a single legend label. Basic Approach Use the HandlerTuple class to group multiple plot objects under one legend entry ? import matplotlib.pyplot as plt from matplotlib.legend_handler import HandlerTuple plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create two different line plots p1, = plt.plot([1, 2.5, 3], 'r-d', label='Red line') p2, = plt.plot([3, 2, 1], 'k-o', label='Black line') # Create legend with both keys for ...
Read MoreRemoving Horizontal Lines in image (OpenCV, Python, Matplotlib)
Removing horizontal lines from images is useful for preprocessing scanned documents or cleaning up images with unwanted line artifacts. We'll use OpenCV's morphological operations to detect and remove these lines while preserving the important content. Understanding the Process The horizontal line removal process involves several key steps ? Convert the image to grayscale and apply thresholding Use horizontal morphological kernels to detect line structures Find contours of the detected lines Remove the lines by drawing over them Apply repair operations to restore nearby content Complete Implementation Here's a complete example that demonstrates horizontal ...
Read MoreSpecifying the line width of the legend frame in Matplotlib
To specify the line width of the legend frame in Matplotlib, we can use the set_linewidth() method. This allows you to customize the appearance of legend lines independently from the actual plot lines. Basic Example Here's how to modify the line width of legend entries ? import numpy as np import matplotlib.pyplot as plt # Set figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create data points x = np.linspace(-5, 5, 100) y = np.sin(x) # Create plot fig, ax = plt.subplots() ax.plot(x, y, c='r', label='y=sin(x)', linewidth=3.0) # Create legend ...
Read MoreHow to plot a Pandas multi-index dataFrame with all xticks (Matplotlib)?
When working with multi-index DataFrames in Pandas, plotting with proper x-axis tick labels can be challenging. This guide shows how to create readable plots with all x-tick labels displayed correctly using Matplotlib. Understanding Multi-Index DataFrames A multi-index DataFrame has multiple levels of row or column indices. When plotting such data, the default x-axis labels might not display all information clearly ? Step-by-Step Implementation Creating Sample Multi-Index Data First, let's create a multi-index DataFrame with time-series data grouped by year and month ? import numpy as np import matplotlib.pyplot as plt import pandas as ...
Read MoreIs it possible to use pyplot without DISPLAY?
Yes, it is possible to use matplotlib pyplot without a display by using a non-interactive backend like Agg. This is particularly useful for server environments or headless systems where no GUI display is available. Setting a Non-Interactive Backend Use matplotlib.use('Agg') before importing pyplot to set a non-interactive backend − import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np # Set figure properties plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create data x = np.linspace(-np.pi, np.pi, 100) # Plot data plt.plot(x, np.sin(x) * x, c='red') # Save figure without displaying ...
Read MoreHow to draw a line outside of an axis in Matplotlib?
When creating matplotlib plots, you may need to draw lines or arrows outside the main plotting area for annotations or visual emphasis. The annotate() method provides a flexible way to add lines outside the axis boundaries. Understanding Coordinate Systems To draw outside the axis, we use xycoords='axes fraction' where coordinates are expressed as fractions of the axis dimensions. Values outside 0-1 range extend beyond the axis boundaries. Basic Example Here's how to draw a horizontal line below the axis ? import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig ...
Read MoreHow to animate the colorbar in Matplotlib?
To animate the colorbar in Matplotlib, you need to update both the image data and colorbar on each frame. This creates dynamic visualizations where the color mapping changes over time. Basic Colorbar Animation Setup First, let's understand the key components needed for animating a colorbar ? import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation from mpl_toolkits.axes_grid1 import make_axes_locatable # Set figure size and layout plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Create figure and axis fig = plt.figure() ax = fig.add_subplot(111) # Create space for colorbar using divider ...
Read MoreHow to use multiple font sizes in one label in Python Matplotlib?
To use multiple font sizes in one label in Python Matplotlib, you can combine different text elements or use LaTeX formatting with size commands. This allows creating visually appealing labels with varying emphasis. Method 1: Using LaTeX Size Commands The most effective way is using LaTeX size commands like \large, \small, and \huge within a single title ? import numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [8, 4] plt.rcParams["figure.autolayout"] = True x = np.linspace(-5, 5, 100) y = np.cos(x) plt.plot(x, y, 'b-', linewidth=2) # Multiple font sizes in one title ...
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